Are AI assistants deciding which warehouse automation vendors make the enterprise shortlist?

Who buys warehouse automation, and what is one enterprise customer worth?

Operations and supply chain executives with a capital budget, and one customer can mean a multi-site, multi-year program.

The buyer is rarely one person. In the 2026 robotics survey, 41% of respondents were in corporate management and 20% were logistics leaders, and operations owned the robots in 63% of companies that had them. Engineering, IT, finance and procurement join as the project grows. Forrester’s 2026 State of Business Buying (opens in a new tab) found the typical business purchase now involves 13 internal stakeholders and nine external influencers, and procurement was a decision-maker in 53% of buying cycles.

The reason to buy is labor. Asked for the single most important factor, 67% of respondents in the robotics survey named labor costs and 33% labor availability. The business case is judged on return on investment (63%), payback time (52%) and total cost of ownership (47%).

Deal sizes are large and lumpy. Public vendor results show the shape:

The value of a customer continues after go-live. Symbotic’s software revenue grew 57% year over year to $9.3 million in its fiscal fourth quarter, and its operations services revenue grew 21%. In the robotics survey, 35% of companies already had funded new initiatives in progress, and 46% expected to use more than five types of robots within three years. Our inference: the first system is the entry ticket to a customer’s later sites and upgrades, so the cost of losing the first evaluation is larger than the first contract.

Where do AI assistants already sit in an automation project?

At the start, when teams learn which technologies exist and which vendors to call, and again for the business case.

No published survey yet measures AI use among warehouse automation buyers specifically. The closest evidence comes from engineers and from business buyers in general:

Two patterns matter for an automation vendor. First, buyers use AI to start and then verify: engineers rated AI answers 4.7 out of 10 for trust, and Forrester describes generative AI searches as the starting point for business buyers, followed by validation from trusted people and sources. Second, warehouse projects begin with a long learning phase. Among companies still planning robotics, 47% were gathering information, 13% were developing strategy and only 12% were finalizing the business case and capital approval. That learning phase is where an assistant explains what a shuttle system, a cube storage grid or an AMR fleet is, and whose products are examples. Robot makers selling into factories face a similar research phase, covered in how robot makers reach manufacturer shortlists.

Supply chain leaders are also primed to ask. In the 2026 MHI and Deloitte report, 48% rated the disruptive impact of AI as significant or greater, up 25 points from 2025, and 39% said the same of robotics and automation, up 16 points.

Which questions do operations leaders ask AI about warehouse automation?

Questions about fit, payback, risk and alternatives, usually with the site’s own constraints written in.

The prompts below were written by us to show the kinds of questions an operations or engineering team might ask. They are examples, not recorded buyer prompts or observed AI answers.

The risk question is not hypothetical. Interact Analysis noted (opens in a new tab) that Attabotics filed for bankruptcy in 2025 and that other vendors divested or closed units, including Zebra’s robotics division. Buyers committing capital for a decade want evidence of a vendor’s staying power, and an assistant will answer with whatever it can find.

Funding questions are live too. In the robotics survey, planners split between hybrid capital and operating models (36%), pure capital purchase (36%) and robotics as a service (29%). A vendor whose commercial models are not described anywhere public cannot be matched to the buyer’s preferred one.

How does an AI answer become a signed automation project?

Through a technology choice, a longlist, a proposal request, a business case and a pilot, then rollout.

1. Learning and technology choice. The team learns which system types suit its order profile. An assistant’s explanation can push the project toward one technology, and with it toward the vendors that supply it.
2. Longlist. In the robotics survey, buyers leaned on materials handling suppliers (47%), robotics vendors (40%), trade associations (35%) and industry analysts or advisory firms (33%). An assistant now sits beside those sources when the first list is made. The advisory firms on that list are now judged by what AI says about them too, as when CEOs ask AI which consultancy to hire shows.
3. Request for information or proposal. A short list of suppliers and integrators is invited to respond, often with a consultant involved. Integrators face their own shortlist, covered in how automation integrators reach plant leaders.
4. Business case and approval. Simulation, return on investment and payback go to finance. This stage is slow: 47% of planners expected more than two years from project start to go-live.
5. Pilot or proof. Forrester found 78% of buyers making purchases of $10 million or more ran a trial first.
6. Rollout and expansion. Software, service and new sites follow, and in the robotics survey 45% of companies with robots said their budgets were rising.

AI visibility can change the first two steps. Engineering, price, references and delivery decide the rest. But a vendor not on the longlist in step 2 never reaches step 3, and Interact Analysis expects 2026 demand to broaden from a handful of giants to large enterprises and mid-sized companies, many of them buying automation for the first time with no incumbent vendor in mind.

What decides whether an assistant names your system?

The platforms document how they search, not how they choose; studies point to independent coverage and specific, checkable facts.

On the search side, the platforms are open. OpenAI’s help page (opens in a new tab) says ChatGPT search reworks an operations leader’s question into one or more targeted queries for its search providers, and only sites that let in its crawler, OAI-SearchBot, are eligible. Google’s announcement (opens in a new tab) says AI Mode runs several related searches across a question’s subtopics, a technique it names “query fan-out.” Neither publishes how vendors are selected for an answer.

What has been observed in our studies, which covered buyer questions across several industries rather than automation:

• Assistants search for rankings and publications. In our study of hidden searches, ChatGPT ran a mean of 3.7 searches per answer, and in 43.8% of its answers it ran a search aimed at a named publication, ranking or award.
• Google rank is only part of it. In our comparison of AI citations with Google rankings, 8.3% of the pages ChatGPT cited ranked in Google’s top 10 for the question.
• Answers vary between assistants. In our brand agreement study, two assistants’ recommendations for the same question overlapped by only 0.327 on average, on a scale where 1 means identical lists.

Our inference for warehouse automation: the trust factors are the ones an engineering team already checks, written where a machine can read them. That means system types and throughput ranges, storage density and footprint, temperature ranges, the industries and order profiles served, named reference sites (with permission), integrator coverage by region, software interfaces, service and spare-parts commitments, and evidence of financial stability. It also means coverage in the publications engineers trust, which in the GlobalSpec survey edged out vendor websites as their top research destination for the first time.

What does it cost to be missing from the early longlist?

A seat at an evaluation that may decide years of follow-on business; no one has measured the loss directly.

We found no study that measures automation projects lost because a vendor was absent from AI answers, so we set out the reasoning and label it:

• The list forms before contact. Engineers complete 62% of the journey online before talking to a vendor. We infer that a vendor missing from the early research is often missing from the request for proposal, and that the loss shows up as invitations that never arrive.
• Recognition decides close calls. 70% of technical buyers said they would likely pick the better-known brand when two solutions are technically similar, and 53% said familiarity influenced their most recent purchase. With 1,057 exhibitors at MODEX 2026, being remembered is hard, and being named by an assistant is one more place to be remembered.
• Wrong facts filter you out. An assistant that says your system does not handle frozen goods, totes over a certain weight or a given throughput removes you from a project you could win. Our guide to fixing wrong brand information in AI answers explains how to trace an error to the page behind it.
• Expansion follows the first win. Because customers add sites and robot types after a first project, a lost first evaluation can cost the later ones too. This is our inference from the survey and vendor results above.

How does GEO work for a warehouse automation company?

Generative engine optimization (GEO) makes your systems easy for AI assistants to find, describe accurately and verify against outside sources.

For an automation vendor or integrator, the work usually covers:

1. Application pages written as text. One page per use case, such as each-picking for e-commerce, pallet storage in freezers or parcel sortation, with throughput, density, footprint and building requirements stated plainly, not only in brochures, videos or PDFs behind a form.
2. A clear business case method. How you calculate payback and total cost of ownership, which inputs matter and what ranges customers have seen, in public, so an assistant answering a payback question has a source that names you.
3. Commercial models stated. Capital purchase, hybrid and robotics-as-a-service options described where buyers and assistants can find them.
4. One consistent identity. The same company name, product names, partner and integrator lists, regions and certifications on your site, partner sites, association directories and trade show listings.
5. Independent coverage. Features and case studies in trade publications, talks at MODEX and ProMat, association membership, and analyst coverage. Our guide on how brands build authority for AI search explains why outside sources matter.
6. Staying-power evidence. Installed base, years in operation, service network and spare-parts commitments, stated in text and confirmed by outside reporting.
7. Crawl access and measurement. Allow the search crawlers the assistants document, then ask a fixed set of technology, fit and vendor questions across ChatGPT, Gemini, Perplexity, Copilot and Google’s AI features, repeatedly, tracking who is named and which sources are cited.

No one can promise a vendor a place on an AI-built longlist; this work makes your systems easier to find and confirm. Warehouse and transportation software vendors face the same longlist problem, which our articles on logistics software and supply chain software cover. Plant-floor software has its own version, covered in MES and IIoT platforms in AI answers.

What can’t the current data tell an automation vendor?

It shows buyers researching with AI, not how many automation contracts start with an AI answer.

• No automation-specific AI study. The AI usage figures come from engineers in general and from business buyers across industries. Applying them to warehouse automation is our inference.
• Interested parties. The robotics survey reached 166 subscribers of Modern Materials Handling and its sister publications; MHI is a trade association; GlobalSpec sells advertising to industrial firms; vendor figures come from investor reports.
• No attribution. We found no public data linking AI answers to requests for proposal or contract value in this industry.
• Our studies are cross-industry. They covered buyer questions in several sectors, not automation projects, and the platforms change how their assistants search over time.

Where should a warehouse automation company start?

Start by asking assistants the questions a distribution team asks before it writes a request for proposal.

Run those questions for your system types, your target industries and your regions, and look at four things: whether you are named, whether your capabilities and limits are described correctly, which publications and sites the answers cite, and which competitors appear instead. The work that follows is to put your application facts where assistants read them and to earn the outside coverage that confirms them.

If your growth depends on being invited into a few large automation projects each year, talk to us about your AI visibility. We will show where your systems appear when operations leaders research automation with AI, why other vendors are named instead, and which changes are most likely to bring more qualified invitations to bid. The ongoing work, such as application pages in plain text, a public payback method and evidence of staying power, is described on our generative engine optimization service page.

Frequently asked questions

Do warehouse operators really use ChatGPT to research automation?

There is no automation-specific measure yet. Among technical buyers in general, 69% used generative AI somewhere in purchasing in 2026, and 21% routinely researched on AI platforms, up 8 points in a year.

Can a smaller AMR or integrator compete with large automation vendors in AI answers?

It can be named for specific combinations of use case, building type, industry and region where fewer suppliers fit. Large vendors still benefit from recognition, which 70% of technical buyers say tips close decisions.

Should we publish payback and pricing information?

Publishing your payback method and the inputs that drive it gives assistants a source to cite for business case questions. Exact project prices depend on the site, so ranges and worked examples are more useful than list prices.

Does AI search replace trade shows and consultants?

No. Buyers still rely on suppliers, trade associations and advisory firms, and 69% of business buyers prefer to validate AI-generated insights with sales reps. AI changes who is on the first list.

Sources

• Supply Chain 24/7, Peerless Research Group, MHI and The Robotics Group (2026-06-01), 2026 Intralogistics Robotics Survey: Robotics moves into the mainstream (opens in a new tab)
• TREW Marketing and GlobalSpec (2026), State of Marketing to Engineers research report (opens in a new tab)
• MHI and Deloitte via Mann Publications (2026-04-15), New MHI and Deloitte report finds AI is biggest disruptor of supply chains over the next decade (opens in a new tab)
• MHI Solutions (2026-06-26), MODEX 2026 Sets New Record (opens in a new tab)
• Interact Analysis via Material Handling Wholesaler (2026-02-09), Warehouse automation: what to expect in 2026 (opens in a new tab)
• The Motley Fool (2025-11-24), Symbotic (SYM) Q4 2025 Earnings Call Transcript (opens in a new tab)
• AutoStore via MFN (2026-08-13), AutoStore Q2 2026 financial results (opens in a new tab)
• Forrester (2026-01-21), Forrester’s 2026 Buyer Insights: GenAI Is Upending B2B Buying (opens in a new tab)
• Gartner (2026-05-20), Gartner survey finds 69% of B2B buyers turn to sales reps to validate AI-generated insights (opens in a new tab)
• OpenAI Help Center (2026), ChatGPT search (opens in a new tab)
• Google (2025-03-05), Expanding AI Overviews and introducing AI Mode (opens in a new tab)
• Underneath (2026), The hidden searches AI assistants run before they answer
• Underneath (2026), Do ChatGPT, Gemini, Perplexity and Claude cite pages that rank?
• Underneath (2026), Do ChatGPT, Gemini, Perplexity and Claude agree on brands?